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Topological representations of crystalline compounds for the machine-learning prediction of materials properties 被引量:3

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摘要 Accurate theoretical predictions of desired properties of materials play an important role in materials research and development.Machine learning(ML)can accelerate the materials design by building a model from input data.For complex datasets,such as those of crystalline compounds,a vital issue is how to construct low-dimensional representations for input crystal structures with chemical insights.In this work,we introduce an algebraic topology-based method,called atom-specific persistent homology(ASPH),as a unique representation of crystal structures.The ASPH can capture both pairwise and many-body interactions and reveal the topology-property relationship of a group of atoms at various scales.Combined with composition-based attributes,ASPH-based ML model provides a highly accurate prediction of the formation energy calculated by density functional theory(DFT).After training with more than 30,000 different structure types and compositions,our model achieves a mean absolute error of 61 meV/atom in cross-validation,which outperforms previous work such as Voronoi tessellations and Coulomb matrix method using the same ML algorithm and datasets.Our results indicate that the proposed topology-based method provides a powerful computational tool for predicting materials properties compared to previous works.
出处 《npj Computational Materials》 SCIE EI CSCD 2021年第1期240-247,共8页 计算材料学(英文)
基金 The search fnancally suppomad by Soft Sciance Raaaanch Projact Guangdbng Province(20178030301013) Nidanal Kay RD Progam of China(2010700600) Srhen Science and Technology Rsch Gn(705Y5201707281026184) The work of Guo Wei Wa was supportad in padal by NSF Gans DMS1721024,DMSI761320 IS 1900473,NIH grans GMI 26180 and GMI 29004,Bktol-Myars Squibb,and Prer.
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